Priming Mindfulness Project 2

Comparison & analysis report

Introduction

This report describes the results of a preregistered study available at: https://osf.io/582wx.

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Note also that this data has been cleaned beforehand. Three datasets were merged (joined) through an inner join—2 Qualtrics surveys and 1 Inquisit task—so as to keep only participants who at least participated at each step of the study. Missing data will be imputed later on. Duplicates were addressed with the rempsyc::best_duplicate function, which keeps the duplicate with the least amount of missing values, and in case of ties, takes the first occurrence.

Packages & Data

Packages

library(rempsyc)
library(dplyr)
library(interactions)
library(performance)
library(see)
library(ggplot2)
library(report)
library(bestNormalize)
library(psych)
library(visdat)
library(missForest)
library(doParallel)

summary(report(sessionInfo()))

The analysis was done using the R Statistical language (v4.2.1; R Core Team, 2022) on Windows 10 x64, using the packages iterators (v1.0.14), doParallel (v1.0.17), interactions (v1.1.5), performance (v0.10.0.10), see (v0.7.3), report (v0.5.5.1), foreach (v1.5.2), bestNormalize (v1.8.3), psych (v2.2.9), missForest (v1.5), rempsyc (v0.1.0.2), visdat (v0.5.3), ggplot2 (v3.3.6) and dplyr (v1.0.10).

Data

# Read data
data <- read.table("data/fulldataset.txt", sep = "\t", header = TRUE)

# Dummy-code group variable
data <- data %>% 
  mutate(condition_dum = ifelse(condition == "Mindfulness", 1, 0),
         condition = as.factor(condition))

cat(report_participants(data, threshold = 1))

379 participants (Mean age = 42.9, SD = 12.8, range: [21, 81]; Gender: 58.3% women, 40.1% men, 1.58% non-binary; Country: 100.00% United States of America; Race: 75.73% White, 10.55% Black or African American, 7.12% Asian, 4.22% Mixed, 2.37% other)

# Allocation ratio
report(data$condition)

x: 2 levels, namely Control (n = 191, 50.40%) and Mindfulness (n = 188, 49.60%)

Preparation

At this stage, we define a list of our relevant variables.

# Make list of DVs
col.list <- c("blastintensity", "blastduration", "blastintensity.duration",
              "KIMS", "BSCS", "BAQ")

Data cleaning

In this section, we are preparing the data for analysis: (a) taking care of preliminary exclusions, (b) checking for and exploring missing values, (d) imputing missing data with missForest, (e) computing scale means, and (f) extracting reliability indices for our scales.

Preliminary exclusions

First, we only want to keep those who agreed to keep their participation in the study after the debriefing.

data %>% 
  count(debrief.consent)

debrief.consent	n
Yes, I accept to maintain my participation to this study.	379

Nobody to exclude based on consent.

Second, we know that we only want to keep participants who had at least an 80% success rate in the critical experimental manipulation task. Let’s see how many participants have less than an 80% success rate. Those with missing values for variable manipsuccessleft will also be excluded since they have not completed the critical experimental manipulation in this study.

data %>% 
    summarize(success.80 = sum(manipsuccessleft < .80, 
                               na.rm = TRUE),
              is.na = sum(is.na(manipsuccessleft)))

success.80	is.na
10	0

There’s 10 people with success smaller than 80%, let’s exclude them.

data <- data %>% 
    filter(manipsuccessleft >= .80)
cat(report_participants(data, threshold = 1))

369 participants (Mean age = 43.2, SD = 12.8, range: [21, 81]; Gender: 58.8% women, 39.6% men, 1.63% non-binary; Country: 100.00% United States of America; Race: 76.69% White, 9.49% Black or African American, 7.05% Asian, 4.34% Mixed, 2.44% other)

Let’s also exclude those who failed 2 or more attention checks (i.e., keep with those with a score of two or more).

data <- data %>% 
    mutate(att_check = rowSums(
      select(., att_check1, att_check2, att_check3)))

data %>% 
  count(att_check)

att_check	n
1	5
2	17
3	347

There’s 5 more exclusions here.

data <- data %>% 
  filter(att_check >= 2)

cat(report_participants(data, threshold = 1))

364 participants (Mean age = 43.4, SD = 12.7, range: [21, 81]; Gender: 59.1% women, 39.3% men, 1.65% non-binary; Country: 100.00% United States of America; Race: 76.65% White, 9.62% Black or African American, 7.14% Asian, 4.40% Mixed, 2.20% other)

Explore missing data

Missing items

# Check for nice_na
nice_na(data, scales = c("BSCS", "BAQ", "KIMS"))

var	items	na	cells	na_percent	na_max	na_max_percent	all_na
BSCS_1:BSCS_7	7	0	2548	0.00	0	0.00	0
BAQ_1:BAQ_12	12	0	4368	0.00	0	0.00	0
KIMS_1:KIMS_39	39	2	14196	0.01	2	5.13	0
Total	95	18	34580	0.05	2	2.11	0

No missing data for our scales of interest, yeah! Except 2 items for KIMS.

Patterns of missing data

Let’s check for patterns of missing data.

# Smaller subset of data for easier inspection
data %>%
  select(manualworkerId:att_check2_raw, 
         condition:condition_dum) %>%
  vis_miss

Little’s MCAR test

# Let's use Little's MCAR test to confirm
# We have to proceed by "scale" because the function can only
# support 30 variables max at a time
library(naniar)
data %>% 
  select(BSCS_1:BSCS_7) %>% 
  mcar_test

statistic	df	p.value	missing.patterns
0	0	0	1

# a p-value of 0 means the test failed because there's no missing values.

data %>% 
  select(BAQ_1:BAQ_12) %>% 
  mcar_test

statistic	df	p.value	missing.patterns
0	0	0	1

# a p-value of 0 means the test failed because there's no missing values.

data %>% 
  select(KIMS_1:KIMS_20) %>% 
  mcar_test

statistic	df	p.value	missing.patterns
34.45728	19	0.0162216	2

data %>% 
  select(KIMS_21:KIMS_39) %>% 
  mcar_test

statistic	df	p.value	missing.patterns
18.04105	18	0.4529514	2

Impute missing data

…

Here, we impute missing data with the missForest package, as it is one of the best imputation methods.

Imputation

# Need character variables as factors
# "Error: Can not handle categorical predictors with more than 53 categories."
# So we have to temporarily remove IDs also...
new.data <- data %>% 
  select(-c(manualworkerId, embeddedworkerId, gender,
            att_check1, att_check2, att_check3)) %>% 
  mutate(across(where(is.character), as.factor))

# Parallel processing
registerDoParallel(cores = 4)

# Variables
set.seed(100)
data.imp <- missForest(new.data, verbose = TRUE, parallelize = "variables")
# Total time is 2 sec (4*0.5) - 4 cores

# Extract imputed dataset
new.data <- data.imp$ximp

There are some variables we don’t actually want to impute, like country. We want to keep those NAs in that case. Let’s add them back. We also want to add ID back.

# Add ID
new.data <- bind_cols(manualworkerId = data$manualworkerId, new.data)

# Add back the NAs in country
data <- new.data %>% 
  mutate(country.ip = data$country.ip,
         gender = data$gender,
         att_check1 = data$att_check1, 
         att_check2 = data$att_check2,
         att_check3 = data$att_check3)

Details

Why impute the data? van Ginkel explains,

Regardless of the missingness mechanism, multiple imputation is always to be preferred over listwise deletion. Under MCAR it is preferred because it results in more statistical power, under MAR it is preferred because besides more power it will give unbiased results whereas listwise deletion may not, and under NMAR it is also the preferred method because it will give less biased results than listwise deletion.

van Ginkel, J. R., Linting, M., Rippe, R. C. A., & van der Voort, A. (2020). Rebutting existing misconceptions about multiple imputation as a method for handling missing data. Journal of Personality Assessment, 102(3), 297-308. https://doi.org/10.1080/00223891.2018.1530680

Why missForest? It outperforms other imputation methods, including the popular MICE (multiple imputation by chained equations). You also don’t end up with several datasets, which makes it easier for following analyses. Finally, it can be applied to mixed data types (missings in numeric & categorical variables).

Waljee, A. K., Mukherjee, A., Singal, A. G., Zhang, Y., Warren, J., Balis, U., … & Higgins, P. D. (2013). Comparison of imputation methods for missing laboratory data in medicine. BMJ open, 3(8), e002847. https://doi.org/10.1093/bioinformatics/btr597

Stekhoven, D. J., & Bühlmann, P. (2012). MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112-118. https://doi.org/10.1093/bioinformatics/btr597

Scale Means

…

Now that we have imputed the missing data, we are ready to calculate our scale means.

Self-control

# Reverse code items 2, 4, 6, 7
data <- data %>% 
  mutate(across(starts_with("BSCS"), .names = "{col}r"))
data <- data %>% 
  mutate(across(c(BSCS_2, BSCS_4, BSCS_6, BSCS_7), ~nice_reverse(.x, 5), .names = "{col}r"))

# Get mean BSCS
data <- data %>% 
  mutate(BSCS = rowMeans(select(., BSCS_1r:BSCS_7r)))

Trait aggression

# Reverse code item 7
data <- data %>% 
  mutate(across(starts_with("BAQ"), .names = "{col}r"))
data <- data %>% 
  mutate(across(BAQ_7, ~nice_reverse(.x, 7), .names = "{col}r"))

# Get sum of BAQ
data <- data %>% 
  mutate(BAQ = rowMeans(select(., BAQ_1r:BAQ_12r)))

Trait Mindfulness

# Reverse code items 3-4, 8, 11-12, 14, 16, 18, 20, 22, 23-24, 27-28, 31-32, 35-36
data <- data %>% 
  mutate(across(starts_with("KIMS"), .names = "{col}r"))
data <- data %>% 
  mutate(across(all_of(paste0("KIMS_", c(3:4, 8, 11:12, 14, 16, 18, 20,
                                         22:24, 27:28, 31:32, 35:36))), 
                ~nice_reverse(.x, 5), .names = "{col}r"))

# Get sum of KIMS
data <- data %>% 
  mutate(KIMS = rowMeans(select(., KIMS_1r:KIMS_39r)))

Intensity * Duration

# Create new variable blastintensity.duration
data <- data %>% 
  mutate(blastintensity.duration = blastintensity * blastduration)

Reliability

…

Now that we have reversed our items, we can get the alphas for our different scales.

Self-control

x <- data %>% 
  select(BSCS_1r:BSCS_7r) %>% 
  alpha
x$feldt

## 
##    95% confidence boundaries (Feldt)
##  lower alpha upper
##   0.81  0.84  0.86

Trait aggression

x <- data %>% 
  select(BAQ_1r:BAQ_12r) %>% 
  alpha
x$feldt

## 
##    95% confidence boundaries (Feldt)
##  lower alpha upper
##   0.81  0.84  0.86

Trait Mindfulness

x <- data %>% 
  select(KIMS_1r:KIMS_39r) %>% 
  alpha

## Warning in alpha(.): Some items were negatively correlated with the total scale and probably 
## should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option

## Some items ( KIMS_8r ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option

x$feldt

## 
##    95% confidence boundaries (Feldt)
##  lower alpha upper
##   0.89   0.9  0.92

We are getting the “Some items ( KIMS_8r ) were negatively correlated with the total scale and probably should be reversed.” warning. Let’s check these items. In the raw data, item 17 is:

labels.part2$KIMS_8

8. “I tend to evaluate whether my perceptions are right or wrong.”

Which corresponds to items 17 and 19 as described in the original paper (Baer, Smith, & Allen in 2004). They also make sense to not reverse-score, theoretically speaking; they seem to relate to mindfulness, not its absence. So it is kind of strange that psych::alpha is suggesting to reverse-score these items. Perhaps the scale is not meant to be used as a single factor since it contains four factor subscales.

t-tests

In this section, we will: (a) test assumptions of normality, (b) transform variables violating assumptions, (c) test assumptions of homoscedasticity, (d) identify and winsorize outliers, and (e) conduct the t-tests.

Normality

lapply(col.list, function(x) 
  nice_normality(data, 
                 variable = x, 
                 title = x,
                 group = "condition",
                 shapiro = TRUE,
                 histogram = TRUE))

## [[1]]

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## Warning: Removed 1 rows containing non-finite values (stat_bin).

## Warning: Removed 1 rows containing non-finite values (stat_density).

## 
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Several variables are clearly skewed. Let’s apply transformations. But first, let’s deal with the working memory task, SOPT (Self-Ordered Pointing Task). It is clearly problematic.

Transformation

The function below transforms variables according to the best possible transformation (via the bestNormalize package), and also standardizes the variables.

predict_bestNormalize <- function(var) {
  x <- bestNormalize(var, standardize = FALSE, allow_orderNorm = FALSE)
  print(cur_column())
  print(x$chosen_transform)
  cat("\n")
  predict(x)
}

set.seed(100)
data <- data %>% 
  mutate(across(all_of(col.list), 
                predict_bestNormalize,
                .names = "{.col}.t"))

## [1] "blastintensity"
## I(x) Transformation with 364 nonmissing obs.
## 
## [1] "blastduration"
## I(x) Transformation with 364 nonmissing obs.
## 
## [1] "blastintensity.duration"
## Non-Standardized sqrt(x + a) Transformation with 364 nonmissing obs.:
##  Relevant statistics:
##  - a = 0 
##  - mean (before standardization) = 66.62117 
##  - sd (before standardization) = 32.39192 
## 
## [1] "KIMS"
## Non-Standardized asinh(x) Transformation with 363 nonmissing obs.:
##  Relevant statistics:
##  - mean (before standardization) = 1.93303 
##  - sd (before standardization) = 0.1366693 
## 
## [1] "BSCS"
## Non-Standardized Yeo-Johnson Transformation with 364 nonmissing obs.:
##  Estimated statistics:
##  - lambda = 1.59839 
##  - mean (before standardization) = 6.664788 
##  - sd (before standardization) = 1.998245 
## 
## [1] "BAQ"
## Non-Standardized sqrt(x + a) Transformation with 364 nonmissing obs.:
##  Relevant statistics:
##  - a = 0 
##  - mean (before standardization) = 1.712829 
##  - sd (before standardization) = 0.3038779

col.list <- paste0(col.list, ".t")

Note. The I(x) transformations above are actually not transformations, but a shorthand function for passing the data “as is”. Suggesting the package estimated the various attempted transformations did not improve normality in those cases, so no transformation is used. This only appears when standardize is set to FALSE. When set to TRUE, for those variables, it is actually center_scale(x), suggesting that the data are only CENTERED because they need no transformation (no need to be scaled), only to be centered.

Let’s check if normality was corrected.

# Group normality
lapply(col.list, function(x) 
  nice_normality(data, 
                 x, 
                 "condition",
                 shapiro = TRUE,
                 title = x,
                 histogram = TRUE))

## [[1]]

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## [[2]]

## 
## [[3]]

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## [[4]]

## Warning: Removed 1 rows containing non-finite values (stat_bin).

## Warning: Removed 1 rows containing non-finite values (stat_density).

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## [[6]]

Looks rather reasonable now, though not perfect (fortunately t-tests are quite robust against violations of normality).

We can now resume with the next step: checking variance.

Homoscedasticity

# Plotting variance
plots(lapply(col.list, function(x) {
  nice_varplot(data, x, group = "condition")
  }),
  n_columns = 3)

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

Variance looks good. No group has four times the variance of any other group. We can now resume with checking outliers.

Outliers

plots(lapply(col.list, function(x) {
  outliers_plot(data, x, group = "condition", ytitle = x, binwidth = 0.15)
  }),
  n_columns = 2)

## Warning: Removed 1 rows containing non-finite values (stat_bindot).

There are some outliers, but nothing unreasonable. Let’s still check with the 3 median absolute deviations (MAD) method.

data %>% 
  #as.data.frame %>% 
  filter(condition == "Control") %>% 
  find_mad(col.list, criteria = 3)

## There were no outlier based on 3 median absolute deviations.

data %>% 
  #as.data.frame %>% 
  filter(condition == "Mindfulness") %>% 
  find_mad(col.list, criteria = 3)

## 3 outlier(s) based on 3 median absolute deviations for variable(s): 
##  blastintensity.t, blastduration.t, blastintensity.duration.t, KIMS.t, BSCS.t, BAQ.t 
## 
## Outliers per variable: 
## 
## $KIMS.t
##   Row KIMS.t_mad
## 1 139  -4.610287
## 2 163  -3.030456
## 
## $BAQ.t
##   Row BAQ.t_mad
## 1 124  3.146018

There are 3 outliers after our transformations.

Winsorization

Visual assessment and the MAD method confirm we have some outlier values. We could ignore them but because they could have disproportionate influence on the models, one recommendation is to winsorize them by bringing the values at 3 SD. Instead of using the standard deviation around the mean, however, we use the absolute deviation around the median, as it is more robust to extreme observations. For a discussion, see:

Leys, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766. https://doi.org/10.1016/j.jesp.2013.03.013

# Winsorize variables of interest with MAD
data <- data %>% 
  group_by(condition) %>% 
  mutate(across(all_of(col.list), 
                winsorize_mad,
                .names = "{.col}.w")) %>% 
  ungroup()

# Update col.list
col.list <- paste0(col.list, ".w")

Outliers are still present but were brought back within reasonable limits, where applicable.

Standardization

We can now standardize our variables.

data <- data %>%
  mutate(across(all_of(col.list),
                function(x) {
                  as.numeric(scale(x))
                  },
                .names = "{col}.s"))

# Update col.list
col.list <- paste0(col.list, ".s")

We are now ready to compare the group condition (Control vs. Mindfulness Priming) across our different variables with the t-tests.

t-tests

nice_t_test(data, 
            response = col.list, 
            group = "condition") %>% 
  nice_table(highlight = 0.10, width = .80)

## Using Welch t-test (base R's default; cf. https://doi.org/10.5334/irsp.82).
## For the Student t-test, use `var.equal = TRUE`. 
##  

## Warning: Missing values detected. NAs dropped.

Interpretation: There is no effect whatsoever on sound blast. However, there seems to be baseline group differences in terms of self-control, trait aggression, and trait mindfulness, despite the block randomization…

Violin plots

Intensity * Duration

nice_violin(data, 
            group = "condition", 
            response = "blastintensity.duration.t.w.s",
            comp1 = 1,
            comp2 = 2,
            obs = TRUE,
            has.d = TRUE,
            d.y = 1)

Blast Intensity

nice_violin(data, 
            group = "condition", 
            response = "blastintensity.t.w.s",
            comp1 = 1,
            comp2 = 2,
            obs = TRUE,
            has.d = TRUE,
            d.y = 1)

Blast Duration

nice_violin(data, 
            group = "condition", 
            response = "blastduration.t.w.s",
            comp1 = 1,
            comp2 = 2,
            obs = TRUE,
            has.d = TRUE,
            d.y = 1)

Means, SD

Let’s extract the means and standard deviations for journal reporting.

Intensity * Duration

data %>% 
    group_by(condition) %>% 
    summarize(M = mean(blastintensity.duration),
              SD = sd(blastintensity.duration),
              N = n()) %>% 
  nice_table(width = 0.40)

Blast Intensity

data %>% 
    group_by(condition) %>% 
    summarize(M = mean(blastintensity),
              SD = sd(blastintensity),
              N = n()) %>% 
  nice_table(width = 0.40)

Blast Duration

data %>% 
    group_by(condition) %>% 
    summarize(M = mean(blastduration),
              SD = sd(blastduration),
              N = n()) %>% 
  nice_table(width = 0.40)

Moderations (confirmatory)

Let’s see if our variables don’t interact together with our experimental condition. But first, let’s test the models assumptions.

Assumptions

Intensity * Duration

big.mod3 <- lm(blastintensity.duration.t.w.s ~ condition_dum*BSCS.t.w.s
                 # + condition_dum*KIMS.t.w.s + condition_dum*BAQ.t.w.s
               , data = data, na.action="na.exclude")
check_model(big.mod3)

Blast Intensity

big.mod1 <- lm(blastintensity.t.w.s ~ condition_dum*BSCS.t.w.s
                 # condition_dum*KIMS.t.w.s + condition_dum*BAQ.t.w.s
               , data = data, na.action="na.exclude")
check_model(big.mod1)

Blast Duration

big.mod2 <- lm(blastduration.t.w.s ~ condition_dum*BSCS.t.w.s
                 # + condition_dum*KIMS.t.w.s + condition_dum*BAQ.t.w.s
               , data = data, na.action="na.exclude")
check_model(big.mod2)

All the models assumptions look pretty good overall actually, even with all these variables. The lines for linearity and homoscedasticity are a bit skewed but nothing too crazy. Let’s now look at the results.

Moderations

Intensity * Duration

big.mod3 %>% 
  nice_lm(b.label = "B") %>% 
  nice_table(highlight = TRUE)

Blast Intensity

big.mod1 %>% 
  nice_lm(b.label = "B") %>% 
  nice_table(highlight = TRUE)

Blast Duration

big.mod2 %>% 
  nice_lm(b.label = "B") %>% 
  nice_table(highlight = TRUE)

Interpretation: The condition by trait self-control (brief self-control scale, BSCS) interaction does not come up.

Interaction plots

Let’s plot the main significant interaction(s).

Intensity * Duration

interact_plot(big.mod3, pred = "condition_dum", modx = "BSCS.t.w.s", 
              modxvals = NULL, interval = TRUE, x.label = "Condition", 
              pred.labels = c("Control", "Mindfulness"),
              legend.main = "Trait Self-Control")

Blast Intensity

interact_plot(big.mod1, pred = "condition_dum", modx = "BSCS.t.w.s", 
              modxvals = NULL, interval = TRUE, x.label = "Condition", 
              pred.labels = c("Control", "Mindfulness"),
              legend.main = "Trait Self-Control")

Blast Duration

interact_plot(big.mod2, pred = "condition_dum", modx = "BSCS.t.w.s", 
              modxvals = NULL, interval = TRUE, x.label = "Condition", 
              pred.labels = c("Control", "Mindfulness"),
              legend.main = "Trait Self-Control")

Interpretation: The interaction is pretty much the same for all models. Counterintuitively, for people with low self-control, the priming mindfulness condition relates to lower aggression relative to the control condition. In contrast, for people with high self-control, the priming mindfulness condition relates to higher aggression.

Simple slopes

Let’s look at the simple slopes now (only for the significant interaction).

Intensity * Duration

big.mod3 %>%
  nice_lm_slopes(predictor = "condition_dum",
                 moderator = "BSCS.t.w.s",
                 b.label = "B") %>% 
  nice_table(highlight = TRUE)

Blast Intensity

big.mod1 %>%
  nice_lm_slopes(predictor = "condition_dum",
                 moderator = "BSCS.t.w.s",
                 b.label = "B") %>% 
  nice_table(highlight = TRUE)

Blast Duration

big.mod2 %>%
  nice_lm_slopes(predictor = "condition_dum",
                 moderator = "BSCS.t.w.s",
                 b.label = "B") %>% 
  nice_table(highlight = TRUE)

Interpretation: There seems to have no effect of priming mindfulness on blast intensity as a function of self-control.

Moderations (exploratory)

Let’s see if our variables don’t interact together with our experimental condition. But first, let’s test the models assumptions.

Assumptions

Intensity * Duration

big.mod3 <- lm(blastintensity.duration.t.w.s ~ condition_dum*KIMS.t.w.s*BSCS.t.w.s +
                 condition_dum*BAQ.t.w.s*BSCS.t.w.s
               , data = data, na.action="na.exclude")
check_model(big.mod3)

Blast Intensity

big.mod1 <- lm(blastintensity.t.w.s ~ condition_dum*KIMS.t.w.s*BSCS.t.w.s + 
                 condition_dum*BAQ.t.w.s*BSCS.t.w.s
               , data = data, na.action="na.exclude")
check_model(big.mod1)

Blast Duration

big.mod2 <- lm(blastduration.t.w.s ~ condition_dum*KIMS.t.w.s*BSCS.t.w.s + 
                 condition_dum*BAQ.t.w.s*BSCS.t.w.s
               , data = data, na.action="na.exclude")
check_model(big.mod2)

All the models assumptions look pretty good overall actually, even with all these variables. The lines for linearity and homoscedasticity are a bit skewed but nothing too crazy. Let’s now look at the results.

Moderations

Intensity * Duration

big.mod3 %>% 
  nice_lm(b.label = "B") %>% 
  nice_table(highlight = TRUE)

Blast Intensity

big.mod1 %>% 
  nice_lm(b.label = "B") %>% 
  nice_table(highlight = TRUE)

Blast Duration

big.mod2 %>% 
  nice_lm(b.label = "B") %>% 
  nice_table(highlight = TRUE)

Interpretation: The condition by trait self-control (brief self-control scale, BSCS) interaction does not come up.

Interaction plots

Let’s plot the main significant interaction(s).

Intensity * Duration

interact_plot(big.mod3, pred = "condition_dum", modx = "BSCS.t.w.s", 
              modxvals = NULL, interval = TRUE, x.label = "Condition", 
              pred.labels = c("Control", "Mindfulness"),
              legend.main = "Trait Self-Control")

Blast Intensity

interact_plot(big.mod1, pred = "condition_dum", modx = "BSCS.t.w.s", 
              modxvals = NULL, interval = TRUE, x.label = "Condition", 
              pred.labels = c("Control", "Mindfulness"),
              legend.main = "Trait Self-Control")

Blast Duration

interact_plot(big.mod2, pred = "condition_dum", modx = "BSCS.t.w.s", 
              modxvals = NULL, interval = TRUE, x.label = "Condition", 
              pred.labels = c("Control", "Mindfulness"),
              legend.main = "Trait Self-Control")

Interpretation: The interaction is pretty much the same for all models. Counterintuitively, for people with low self-control, the priming mindfulness condition relates to lower aggression relative to the control condition. In contrast, for people with high self-control, the priming mindfulness condition relates to higher aggression.

Simple slopes

Let’s look at the simple slopes now (only for the significant interaction).

Intensity * Duration

big.mod3 %>%
  nice_lm_slopes(predictor = "condition_dum",
                 moderator = "BSCS.t.w.s",
                 b.label = "B") %>% 
  nice_table(highlight = TRUE)

Blast Intensity

big.mod1 %>%
  nice_lm_slopes(predictor = "condition_dum",
                 moderator = "BSCS.t.w.s",
                 b.label = "B") %>% 
  nice_table(highlight = TRUE)

Blast Duration

big.mod2 %>%
  nice_lm_slopes(predictor = "condition_dum",
                 moderator = "BSCS.t.w.s",
                 b.label = "B") %>% 
  nice_table(highlight = TRUE)

Interpretation: There seems to have no effect of priming mindfulness on blast intensity as a function of self-control.

Conclusions

Based on the results, it seems that the predicted interaction between self-control and the priming mindfulness manipulation does not come up.

Package References

report::cite_packages(sessionInfo())

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• Stekhoven DJ (2022). missForest: Nonparametric Missing ValueImputation using Random Forest. R package version 1.5.Stekhoven DJ, Buehlmann P (2012). “MissForest - non-parametric missingvalue imputation for mixed-type data.” Bioinformatics, 28(1),112-118.
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